How to Use the Maestra MCP in Vercel AI SDK

Stream Maestra transcription and voiceover data directly into your Vercel AI SDK interface without waiting for loading spinners.

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Connect Maestra MCP to Vercel AI SDK

Create your Vinkius account to connect Maestra to Vercel AI SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Maestra with Vercel AI SDK

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Real-time media pipeline for Vercel AI SDK

Feed media URLs directly into `upload_media_for_transcription` to trigger background processing. Your frontend captures the status updates immediately. Since the AI SDK handles streaming, you display the transcription progress as it happens. Users see the data arrive before the final file is ready.

Dynamic voiceover injection

Call `list_available_ai_voices` to populate dropdowns in your UI. This lets users pick their preferred tone before you execute the `generate_ai_voiceover` tool. Your app state updates the moment the audio file returns. It keeps your UI in sync with the Maestra backend through every step.

Automated translation workflows

Pass transcript IDs into `translate_transcription` to generate localized versions on the fly. The SDK pushes the new text straight to your components. Use `export_transcription_results` to grab the final data structure. It makes rendering translated subtitles in Next.js or React trivial.

Setup guide

Set up Maestra MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Maestra tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Maestra transactions",
});

console.log(text);
await mcpClient.close();

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Common questions about Maestra MCP in Vercel AI SDK

Install the MCP client package and point it to the Maestra endpoint. You then inject the tools into your generateText function to start calling them directly.

Yes. You trigger the transcription tools via the SDK and stream the results to your frontend. It removes the need for manual polling.

It does. The transport layer works with your edge environment so your media processing logic stays close to your users.

Provide your API key through the standard environment variable setup. The client attaches this to every request automatically.

Your media files are processed in isolated environments. The MCP server only facilitates the API calls and never stores your raw audio or video data locally.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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